Traditionally, healthcare providers have kept all of their patients' information in paper filing systems. That patient information includes, but is not limited to, patients' demographic information (e.g., age, weight, gender, race, income, and geographic location), financial information (e.g., outstanding balances, insurance claims currently being processed, and other account information), and clinical information (e.g., clinician documentation of observations, thoughts and actions, treatments administered, patient history, medication and allergy lists, vaccine administration lists, laboratory reports, X-rays, charts, progress notes, consultation reports, procedure notes, hospital reports, correspondence, and test results). The healthcare providers, or clinicians, that maintain that patient information include, but are not limited to, physicians (Doctors of Medicine (MDs) and Doctors of Osteopathic Medicine (DOs)), dentists, chiropractors, podiatrists, therapists, psychologists, physician assistants, nurses, medical assistants, and technicians.
The manual, paper-based practice of keeping a patient's information, however, is a very inefficient, labor-intensive process that requires many checks and balances to ensure accurate processing of the information and, therefore, takes up a significant amount of clinician's time that could otherwise be spent with patients. Accordingly, electronic medical records (EMRs), Electronic Health Records (EHRs), and Personal Health Records (PHRs) have been developed to provide many of the functionalities and features of paper filing systems in an electronic, paperless format.
An EMR is an electronic record of patient information that can be created, gathered, managed, and consulted by the authorized clinicians and other staff at the healthcare practice where the record is created. An EHR is an electronic record of patient information that conforms to nationally recognized interoperability standards and that can be created, managed, and consulted by authorized clinicians and staff, both at the healthcare practice that creates the record and at other healthcare practice sites. And, a PHR is an electronic record of patient information that conforms to nationally recognized interoperability standards and that can be drawn from multiple sources while being managed, shared, and controlled by the patient to whom it belongs. Accordingly, EMRs are aimed primarily at the efficient management of multiple records in a single healthcare provider's practice, while EHRs and PHRs are aimed primarily at integrating multiple data sources into each electronic record.
The nationally recognized interoperability standards for EHRs are currently endorsed by the Healthcare Information Technology Standards Panel (HTISP) and certified by the Certification Commission for Healthcare Information Technology (CCHIT). Those standards require EHRs to have the ability to communicate and exchange data accurately, effectively, securely, and consistently with different information technology systems, software applications, and networks in various settings such that the clinical or operational purpose and meaning of the data are preserved and unaltered as that data is exchanged. Thus, while an EMR is generally characterized as an electronic version of a physician's paper record, an EHR is characterized as a more comprehensive record containing additional data integrated to and from other sources. EHRs are further characterized as being either “basic” or “fully functional.” A basic EHR includes patient demographics, problem lists, clinical notes, orders for prescription, and viewing laboratory and imaging results. A fully functional EHR includes patient demographics, problem lists, clinical notes, medical history and follow-up, orders for prescriptions, orders for tests, prescription orders sent electronically, laboratory and imaging results, warnings of drug interactions or contraindications, out-of-range test levels, and reminders for guideline-based interventions.
At their core, EMR and EHR systems include large-capacity databases that contain patient information stored in structured, relational tables of searchable data. Unfortunately, many of the vendors of EMR and EHR systems have resisted making their software capable of exporting and importing patient information using uniform electronic messaging, document, and form management standards (e.g., the Health Level Seven (HL7) messaging standard, the Continuity of Care Document (CCD) document standard, and the Retrieve Form for Data Capture (RFD) form management standard). And, when data is not captured and stored using uniform, standardized medical vocabularies, and when it is not transmitted using uniform messaging, document, and form management standards, that data of little use outside of the system in which it is captured and stored. Instead, custom interfaces must be designed to allow the import and export of data between systems so that data can be shared between those systems. The process of developing different interfaces between the disparate formats used by different vendors is expensive and difficult. Moreover, such interfaces are also costly and labor-intensive to maintain.
The problem of interfacing different EMR and EHR systems is exacerbated by the fact that, in the present health care industry, most patient visits are to small, self-contained practices that often treasure their autonomy and are unwilling and/or unable to acquire EMR and EHR systems unless each of those systems is individually tailored to the narrow objectives of each specific self-contained practice. Accordingly, most EMR and EHR vendors have been forced to provide healthcare practices with individually customized systems that employ stand-alone features and functions on the basis of what a specific practice group wants and needs, which means that similar practice groups in adjacent counties may have very different system features and functions based on their different priorities. Thus, the various existing EMR and EHR systems are not well suited for interaction and data exchange with each other, or for maintaining information that would be useful to other systems. The data collected by the different practice groups using EMR and EHR systems is therefore severely fragmented.
In addition, most of the commercially available EMR and EHR systems have not been well received by healthcare providers. In fact, according to a 2008 survey conducted by the National Center for Health Services (NCHS), a division of the Centers for Disease Control and Prevention (CDC), while about 40% of U.S. office-based physicians reported using EMR systems, only 17% reported using basic EHR systems, and only 4% reported using fully functional EHR systems. Healthcare providers tend to resist such systems because those systems are unable to keep up with the workflow demands of clinicians during the various tasks they perform throughout the day. Traditional EMR and EHR systems are generally technology-driven, as opposed to being user-driven. Accordingly, healthcare providers find them difficult to use, especially those healthcare providers that have difficulty with computer technology, and especially when it involves adopting new software with which the healthcare provider is not already familiar. Many healthcare providers would rather focus solely on patient care than be bothered with learning how to operate the latest computer technology.
In an attempt to gain wider acceptance of EMR and EHR systems, some health information technology (HIT) engineers have developed user interfaces to help ease healthcare providers' transition into the electronic record-keeping medium. For example, because healthcare is a dictation-intensive field, some HIT engineers have adopted a speech recognition approach for interfacing with EMR and EHR systems. That approach allows healthcare providers to dictate information as they traditionally have done, except that the information is captured in a computer-readable medium (e.g., an XML file) that can be input directly into EMR and EHR systems. Two different types of speech recognition technology have been developed to help ease healthcare providers' transition into the electronic record-keeping medium and improve turnaround time in generating electronic patient records—back-end speech recognition and front-end speech recognition.
Back-end speech recognition generates an electronic text document in the background as a healthcare provider dictates without the healthcare provider being able to see or edit, and oftentimes without the healthcare provider even being aware of, what is being transcribed in the electronic text document. The resulting electronic text document, along with the corresponding voice file, is then sent to a medical transcription/editing service that reads the electronic text document, listens to the voice file, and corrects any mistakes (recognition and/or dictation) in the electronic text document. The medical transcription/editing service then returns the corrected electronic text document to the healthcare practice for entry into an EMR or EHR system. The medical transcription/editing service may also enter the appropriate information into the EMR or EHR system themselves. And, if the information captured by back-end speech recognition is used to generate documentation that requires the healthcare provider's signature (e.g., progress notes, consultation reports, procedure notes, hospital reports, etc.), that documentation will also need to be provided to the healthcare provider for review and signature. The turnaround time required for a medical transcription/editing service to review and correct the electronic text document is unpredictable and inconvenient. Using such services also creates an additional expense for healthcare providers, who already suffer from large overhead costs.
Front-end speech recognition provides faster turnaround times and eliminates the need for medical transcription/editing services by allowing the healthcare provider to view and edit the electronic text document as it is generated. Thus, instead of using medical transcription/editing services to review and edit the electronic text document, the healthcare provider can immediately see and correct any mistakes (recognition and/or dictation) in the electronic text document Like traditional EMR and EHR systems, however, traditional front-end speech recognition is often provided as separate software that must be interfaced with the EMR or EHR system with which it is being used. Thus, unlike back-end speech recognition running in the background, healthcare providers must familiarize themselves with, and ultimately accept, that new software platform for it to be of any beneficial use.
Although back-end speech recognition does not require healthcare providers to familiarize themselves with and accept new software, neither the software used to provide traditional back-end speech recognition nor the software used to provide traditional front-end speech recognition incorporates uniform electronic messaging, document, and form management standards to import and export the information captured therewith. Instead, the information captured by that software is typically only used to complete the specific clinical documentation for which it was captured (e.g., progress notes, consultation reports, procedure notes, hospital reports, etc.) rather than being provided in a format that can be used for other purposes, such as data collection and analysis for practice management and medical research. And, as discussed above, when data is not captured and stored using uniform, standardized medical vocabularies, and when it is not transmitted using uniform messaging, document, and form management standards, that data is of little use outside of the system in which it was captured unless custom interfaces are designed to connect that system to other systems. Much less, it is of little use outside of the document for which it was captured.
Another shortcoming of conventional speech recognition technology is that such technology requires a significant amount of voice recognition training by a user for the speech recognition to be accurate. For example, a user will be required to dictate several passages into a computer to “train” the voice recognition software on that computer to recognize that user's voice and mannerisms. A “voice profile” is created for that specific user based on that training. The user must either save that voice profile to a portable electronic storage medium (e.g., a CD-ROM or zip disk) or perform the training again whenever that user uses a different computer, or accesses the computer remotely, to dictate speech. Accordingly, management of a user's voice profile can become problematic and burdensome when a user frequently uses different systems to dictate speech.
Because most EMR and EHR systems, and the speech recognition software with which they are interfaced, are not capable of exporting and importing patient information in a standardized format, and because they do not utilize functions and features suited for interaction and data exchange with other systems, the fragmented pools of data collected using those systems cannot easily be combined. Accordingly, an individual healthcare practice cannot share data between its individually customized systems in a way that streamlines management of that healthcare practice, but instead must capture, store, and manage duplicate sets of data between its disparate, stand-alone systems. Moreover, researchers cannot easily collect data from multiple healthcare practices for performing medical research, maintaining disease registries, tracking patient care for quality and safety initiatives, and performing composite clinical and financial analytics. Instead, those processes remain time-consuming and expensive. For example, a clinical research organization (CRO) tasked with identifying patients that satisfy specific criteria for participating in a clinical trial must still sort through voluminous libraries of paper medical records and unstructured data, spending large amounts of time and money searching for candidates.
Those problems are compounded by the regulations of the Health Insurance Portability and Accountability Act (HIPAA). The implementation of the regulations of HIPAA has increased the overall amount of paperwork and the overall costs required for healthcare providers to operate. And the complex legal implications associated with those regulations have caused concerns with compliance among healthcare providers. With regard to researchers, the HIPAA regulations have hindered their ability to perform retrospective, chart-based research as well as their ability to prospectively evaluate patients by contacting them for follow-up surveys. The HIPAA regulations have also led to significant decreases in patient accrual for research, increases in time spent recruiting patients for research, and increases in mean recruitment costs. And by requiring that informed consent forms for research studies include extensive detail on how the participant's protected information will be kept private, those already complex documents have become even less user-friendly.
Accordingly, there is a need for a medical software system that seamlessly integrates the systems required to manage the different activities performed at a healthcare practice (i.e., an EMR or EHR system, a patient registration system, a scheduling system, an account management system, a billing system, etc.) so that duplicate and/or inconsistent data is not captured, stored, and managed by disparate, stand-alone systems. There is also a need for that integrated medical software system to include embedded speech understanding functionality for capturing data in a cost-effective and user-friendly manner. And there is a need for a plurality of such systems for systematically analyzing, collecting, and tracking patient data across a vast patient population (e.g., a community, region, state, nation, etc.) while complying with HIPAA regulations.